Prediction Model for the Stored-Grain Situation Risk Point Based on Broad Learning Network

An accurate prediction for the stored-grain situation is a necessary means to ensure grain security. It is difficult for traditional machine learning methods to process large-scale stored-grain monitoring data, while deep learning methods have the problems of difficult model training and high resource consumption. To solve these problems, a risk prediction model for stored-grain situations was proposed based on a broad learning network. First, the concept of stored-grain risks was proposed and defined in three categories: low, medium, and high. Then, based on the existing broad learning network, two improved broad learning algorithms were proposed: an enhanced node incremental algorithm and an input data incremental algorithm. Based on the multi-modal features of stored-grain situation data, canonical correlation analysis is introduced to the feature extraction and fusion methods. Finally, an accurate prediction model of stored-grain risk based on improved broad learning and correlation analysis is constructed. The experimental results show that, compared to the traditional broad learning model, the two improved broad learning models improve the accuracy of stored-grain risk prediction by more than 2.3%. Meanwhile, compared with the existing deep learning models, the improved broad learning model reduces training time by more than 20 times without reducing the prediction accuracy and has better robustness. In short, the incremental learning training method can make the prediction accuracy of the broad learning model close to or reach the level of the deep learning model with the increase of training data, which proved that the proposed methods may be an effective alternative to deep learning models.